Polyelectrolyte Multilayer Films as Substrates for Photoreceptor Cells
Bibliographic record
Abstract
Reconstruction of extracellular matrix substrates for delivery of functional photoreceptors is crucial in pathologies such as retinal degeneration and age-related macular degeneration. In this study, we assembled polyelectrolyte films using the layer-by-layer deposition method. The buildup of three different films composed of poly(L-lysine)/chondroitin sulfate (PLL/CSA), poly(L-lysine)/poly(styrenesulfonate) (PLL/PSS), or poly(L-lysine)/hyaluronic acid (PLL/HA) was followed by means of quartz crystal microbalance measurements, optical waveguide light mode spectroscopy, confocal microscopy, and atomic force microscopy. The exponential growth regime and the diffusion of PLL chains from the bulk through the PLL/CSA, PLL/PSS, and PLL/HA films was examined. Evaluation of photoreceptor cell viability was optimal on one layer of PLL (PLL(1)), followed by 10 bilayers of PLL/HA [(PLL/HA)(10)] and 10 bilayers of PLL/CSA [(PLL/CSA)(10)]. The number of bilayers and the type of terminating layer also had a significant influence on the number of photoreceptor cells attached. Functionalized polyelectrolyte multilayer films were obtained by adsorbing basic fibroblastic factor (bFGF) or the insoluble fraction of interphotoreceptor matrix (IPM) on or within polyelectrolyte multilayers. bFGF and IPM adsorption on top of the (PLL/CSA)(10)/PLL polyelectrolyte films increased the number of photoreceptor cells attached and maintained the differentiation of rod and cone cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".